| cisimult {lmreg} | R Documentation |
Simultaneous confidence intervals in a linear model
Description
Produces two-sided Bonferroni and Scheffe simultaneous confidence intervals, together with corresponding single confidence intervals, for any vector of estimable functions A.beta in a linear model.
Usage
cisimult(y, X, A, alpha, tol=sqrt(.Machine$double.eps))
Arguments
y |
Responese vector in linear model. |
X |
Design/model matrix or matrix containing values of explanatory variables (generally including intercept). |
A |
Coefficient matrix (A.beta is the vector for which confidence interval is needed). |
alpha |
Collective non-coverage probability of confidence intervals. |
tol |
A relative tolerance to detect zero singular values while computing generalized inverse, in case X is rank deficient (default = sqrt(.Machine$double.eps)). |
Details
Normal distribution of response (given explanatory variables and/or factors) is assumed.
Value
The three sets of confidence intervals listed as below:
BFCB |
Two-sided Bonferroni simultaneous confidence intervals. |
SFCB |
Two-sided Scheffe simultaneous confidence intervals. |
SNCB |
The single confidence intervals. |
Author(s)
Debasis Sengupta <shairiksengupta@gmail.com>, Jinwen Qiu <qjwsnow_ctw@hotmail.com>
References
Sengupta and Jammalamadaka (2019), Linear Models and Regression with R: An Integrated Approach.
Examples
data(denim)
attach(denim)
X <- cbind(1, binaries(Denim), binaries(Laundry))
A <- rbind(c(0,1,-1,0,0,0,0), c(0,1,0,-1,0,0,0), c(0,0,1,-1,0,0,0))
cisimult(Abrasion, X, A, 0.05, tol = 1e-10)
detach(denim)